Grad-CAM++ (G+) [1]} is an extension of Grad-CAM combining the
positive partial derivatives of feature maps of a convolutional layer with a weighted special class score. The weights \(\alpha _c^{(k)}\)  associated to each feature map is computed as follow :
\(\alpha _k^c =\sum _i \sum _j [\frac{ \frac{\partial ^2 {f}(x) }{ (\partial {A}_{ij}^{(k)})^2 } }{ 2 \frac{\partial ^2 {f}(x) }{ (\partial {A}_{ij}^{(k)})^2 } + \sum _i \sum _j {A}^{(k)}_{ij} \frac{\partial ^3 {f}(x) }{ (\partial {A}_{ij}^{(k)})^3 } }]\) 
